Hasil untuk "Large industry. Factory system. Big business"

Baotong Chen, J. Wan, Lei Shu et al.

Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.

F. Longo, A. Padovano, Steven Umbrello

Although manufacturing companies are currently situated at a transition point in what has been called Industry 4.0, a new revolutionary wave—Industry 5.0—is emerging as an ‘Age of Augmentation’ when the human and machine reconcile and work in perfect symbiosis with one another. Recent years have indeed assisted in drawing attention to the human-centric design of Cyber-Physical Production Systems (CPPS) and to the genesis of the ‘Operator 4.0’, two novel concepts that raise significant ethical questions regarding the impact of technology on workers and society at large. This paper argues that a value-oriented and ethical technology engineering in Industry 5.0 is an urgent and sensitive topic as demonstrated by a survey administered to industry leaders from different companies. The Value Sensitive Design (VSD) approach is proposed as a principled framework to illustrate how technologies enabling human–machine symbiosis in the Factory of the Future can be designed to embody elicited human values and to illustrate actionable steps that engineers and designers can take in their design projects. Use cases based on real solutions and prototypes discuss how a design-for-values approach aids in the investigation and mitigation of ethical issues emerging from the implementation of technological solutions and, hence, support the migration to a symbiotic Factory of the Future.

Lida Xu, Lian Duan

Abirami Raja Santhi, Padmakumar Muthuswamy

The Industrial Revolution can be termed as the transformation of traditional industrial practices into new techniques dominated by the technologies available at that time. The first three industrial revolutions were driven respectively by mechanization, electrification, and automation which had gradually transformed the agrarian economy into a manufacturing-based economy. It helped in enhancing the lifestyle of the factory workers and the healthcare system, which improved the overall quality of living. The industries that adapted to the change witnessed a tremendous increase in the production of goods, competitive advantage, and cross-border business opportunities. While we are currently living to see the fourth industrial revolution (also known as Industry 4.0) unfolding around us, the world is poised for the next big leap, the fifth industrial revolution or Industry 5.0. Hence, the first half of the paper outlines the enabling technologies of Industry 4.0 and conceptualizes how they would act as the foundation for the fifth industrial revolution. The socio-economic challenges of the technologies and the need for Industry 5.0 technologies are also discussed. The second half of the paper outlines the prospective technologies of Industry 5.0, their potential applications from the perspective of industry leaders and scholars and conceptualizes how they can overcome the challenges of Industry 4.0. The definition of “sustainability trilemma” a new term coined by the authors, and the reasoning for calling the next industrial revolution “Industry 4.0S” (another new term) rather than Industry 5.0 are also presented.

Liangzhi Li, K. Ota, M. Dong

With the rapid development of Internet of things devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model in this paper, which can find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network model to the fog computing environment, which significantly improves its computing efficiency. The other one is that we work out an inspection model, which can simultaneously indicate the defect type and its degree. The experiments well prove that the proposed method is robust and efficient.

Leonidas Theodorakopoulos, Aristeidis Karras, Alexandra Theodoropoulou et al.

Enterprise decision making in marketing, finance, and audit remains fragmented, leading to inefficient budget allocation and incomplete risk assessment. This study proposes an integrated, Big Data-driven decision-support framework that unifies Large Language Models (LLMs), attention-based marketing mix modeling, and multi-agent, game-theoretic optimization to coordinate cross-functional decisions. The architecture combines five modules: LLM-enhanced customer segmentation and customer lifetime value prediction, attention-weighted marketing mix modeling, multi-agent LLM systems for hierarchical budget optimization, attention-informed Markov multi-touch attribution, and LLM-augmented audit quality assessment. Empirical validation on a large-scale e-commerce dataset with 2.8 million customers and USD 156 million in marketing expenditure shows that marketing return on investment increases from 4.2 to 6.78 (61.4% relative improvement), financial forecasting error (MAPE) decreases from 12.8% to 4.7% (63.3% reduction), fraud detection accuracy improves by 29.8%, the Audit Quality Index reaches 0.951, and customer lifetime value prediction accuracy improves from 76.4% to 91.3%. By operationalizing the convergence of LLMs, attention mechanisms, and game-theoretic reasoning within a unified and empirically validated framework, the study delivers both theoretical advances and practically deployable tools for integrated business intelligence in digital economies.

Md Nazmuddin, Moin Khan

Artificial Intelligence (AI) is transforming the way businesses operate across various sectors, including healthcare, finance, supply chain management, and organisational development. This shift is all about harnessing the power of big data to create more intelligent and efficient systems that benefit both organisations and their customers. In healthcare, for instance, AI is playing a pivotal role in improving diagnostics and patient care. From predicting health outcomes to personalising treatment plans and accelerating drug discovery, AI is making healthcare more efficient and effective. In the finance sector, AI is reshaping how institutions interact with their clients. With advanced algorithms, financial services can offer personalised advice, manage risks more effectively, detect fraud, and ensure compliance with regulations. Companies are using AI-driven tools to engage with customers more effectively and automate various processes, allowing them to allocate resources more efficiently and adopt sustainable practices. Concerns about data privacy, potential biases in algorithms, the complexity of integrating new technologies, workforce adaptation, and regulatory compliance are all hurdles that need to be addressed. Looking ahead, the future of AI seems promising, especially as it converges with other emerging technologies and sustainability initiatives. By embracing AI thoughtfully, organisations can not only gain a competitive edge but also build operational resilience and create lasting value. Ultimately, it’s crucial to find a balance between technological advancement and ethical responsibility. When implemented correctly, AI-driven analytics can pave the way for more equitable, efficient, and intelligent systems across various industries, ultimately benefiting everyone involved.

Wentao Mo, Zhi Zhang, Haidan Wang et al.

As large models continue to gain influence, their application is expanding beyond everyday life and work into traditional industries, including the power sector. The power industry, with its high demands for precision in mechanisms and logical reasoning, presents challenges for large models, which are still evolving and prone to issues like hallucination—weaknesses that cannot be tolerated in critical power systems. To facilitate the practical deployment of large models in the power industry, it is essential to decouple the stable components of artificial intelligence (AI) technologies, such as application scenarios, knowledge bases, and intelligent agents, from their rapidly advancing elements, like models and computational power. One promising approach to achieve this decoupling is through the establishment of Human-AI Collaborative Business (Double Intelligence Collaboration). In this framework, tasks that are well-suited for AI are handled by AI with human oversight, while tasks that AI is not yet capable of autonomously managing are performed by humans with AI assistance. This paper explores how such a decoupling strategy, through human-AI collaboration, can ensure both high reliability and effective application of AI technologies in the power industry, facilitating their integration while minimizing risks. At the same time, it leaves room for future opportunities to adopt rapidly evolving AI technologies.

Shanqi Zhan, Yujuan Qiu

The rapid development of big data analytics has revolutionized data analysis and decision-making processes across industries. This paper explores how to use Apache Spark to analyze the MovieLens 20M dataset and identify the top movies in Minnesota. By integrating robust data preprocessing and collaborative filtering techniques, a novel recommendation system is developed. The results reveal the popular movies in Minnesota, major genres such as drama and comedy, and related tags such as “original” and “finale.” Additionally, a detailed tag correlation analysis is conducted to optimize recommendation accuracy. The study further illustrates Spark's application in large-scale data processing, demonstrating its effectiveness in recommendation systems. These findings bridge the gap between theoretical frameworks and practical applications, providing a replicable approach to address challenges in preprocessing, analysis, and personalized recommendations.

Qaium Hossain, Fahmida Yasmin, Tapos Ranjan Biswas et al.

In the era of big data, organizations are increasingly leveraging advanced analytics to extract valuable insights from vast and complex datasets. Management Information Systems (MIS) play a crucial role in collecting, processing, and analyzing data to support decision-making. Integrating big data analytics into MIS can enhance business intelligence and improve organizational performance. This study aims to investigate the integration of big data analytics into MIS and its impact on business intelligence. Specifically, the study seeks to identify the challenges and opportunities associated with this integration and explore best practices for implementation. A qualitative research approach was adopted for this study. Data was collected through semi-structured interviews based on a survey of over 312 information technology (IT) professionals from 21 industries with IT managers and business analysts from January 2022 to December 2023. Thematic analysis was used to analyze the data and identify key themes related to integrating big data analytics into MIS. The findings indicate that integrating big data analytics into MIS can significantly improve business intelligence. According to the respondents, on average, there was a 30% increase in the accuracy of decision-making processes after the integration. Additionally, organizations reported a 25% reduction in operational costs and a 20% increase in revenue as a result of the integration. Moreover, 70% of the respondents agreed that integrating big data analytics into MIS improved their organization's overall performance. Integrating big data analytics into MIS offers numerous benefits, including improved decision-making, cost savings, and revenue growth. However, organizations must overcome challenges such as data privacy and security concerns and the need for skilled personnel to manage and analyze big data. Overall, this study highlights the importance of integrating big data analytics into MIS for enhancing business intelligence and achieving organizational success.

M. Pourmatin, Abouzar Ilkhani

This study examines the operational problems that a mid-sized food production factory faces. It utilizes system dynamics (SD) and lean thinking approaches to analyze various challenges that these companies encounter. Through extensive data analysis and managerial meetings, this study identifies several factors affecting the company’s performance: cash management, delivery delays, product quality impact on customer satisfaction, debt burden from raw materials purchase, and poor product planning. Various scenarios are simulated to significantly improve sales’ stability and liquidity. This paper offers insights into how effective cash flows can be attained, as well as customer satisfaction, managing debts, and strategic plans that lead to business success. Additionally, the potential for extending SD to address similar challenges in the food industry is explored through detailed financial modeling and actionable plans.

A. Aljumah, M. Nuseir, Md. Mahmudul Alam

PurposeThe aim of the study is to examine the impact of the big data analytics capabilities (BDAC) on the organizational performance. The study also examines the mediating role of ambidexterity and the moderating role of business value of big data (BVBD) analytics in the relationship between the big data analytics capabilities and the organizational performance.Design/methodology/approachThis study collected primary data based on a questionnaire survey among the large manufacturing firms operating in UAE. A total of 650 questionnaires were distributed among the manufacturing firms and 295 samples were used for final data analysis. The survey was conducted from September to November in 2019, and data were analyzed based on partial least squares structural equation modeling (PLS-SEM).FindingsThe big data analysis (BDA) scalability is supported by the findings on the performance of firm and its determinants such as system, value of business and quality of information. The roles of business value as a moderator and ambidexterity as mediator are found significant. The results reveal that there is a need for managers to consider the business value and quality dynamics as crucial strategic objectives to achieve high performance of the firm.Research limitations/implicationsThe study has significant policy implication for practitioners and researchers for understanding the issues related to big data analytics.Originality/valueThis is an original study based on primary data from UAE manufacturing firms.

Fatemeh Ehsani, Monireh Hosseini

Consumer segmentation is an electronic marketing practice that involves dividing consumers into groups with similar features to discover their preferences. In the business-to-customer (B2C) retailing industry, marketers explore big data to segment consumers based on various dimensions. However, among these dimensions, the motives of location and time of shopping have received relatively less attention. In this study, we use the recency, frequency, monetary, and tenure (RFMT) method to segment consumers into 10 groups based on their time and geographical features. To explore location, we investigate market distribution, revenue distribution, and consumer distribution. Geographical coordinates and peculiarities are estimated based on consumer density. Regarding time exploration, we evaluate the accuracy of product delivery and the timing of promotions. To pinpoint the target consumers, we display the main hotspots on the distribution heatmap. Furthermore, we identify the optimal time for purchase and the most densely populated locations of beneficial consumers. In addition, we evaluate product distribution to determine the most popular product categories. Based on the RFMT segmentation and product popularity, we have developed a product recommender system to assist marketers in attracting and engaging potential consumers. Through a case study using data from massive B2C retailing, we conclude that the proposed segmentation provides superior insights into consumer behavior and improves product recommendation performance.

behrooz lotfi, Zahra Mohamadvandi Azar, afsaneh mozaffari

Background and objective. There is currently a strong desire to understand the nature of governance to improve public sector performance, as governance is a critical necessity to help governments realize their development agendas. For example, in recent years in Iran, the approach of good governance has been mentioned as a necessity for economic development in the country.Methodology/approach. The current research is an mixed method study. In the qualitative part, 11 experts familiar with the subject were interviewed to reach theoretical saturation. In the quantitative section, the views of 70 managers and experts of the Ministry of Cooperation, Labor and Social Welfare were used. Data analysis was done in the qualitative part using grounded theory and in the quantitative part using the partial least squares method.Findings and Conclusion. The research findings showed that organizational accountability and the fight against rent and corruption affect the implementation of good governance. The research findings showed that organizational responsibility and combating rent and corruption affect the implementation of good governance. The organization should be responsible to civil society from different aspects. In fact, the organization must be responsive in in terms of the rule of law, transparency, client demands, and its economic and functional areasOriginality/innovation. In this study, for theoretical synergy, an integrated and executive model was presented for good governance in the context of strategic communication in the Ministry of Cooperatives, Labor and Social Welfare. This is despite the fact that previous studies dealt with these concepts separately.

Xinyi Song, Dingkang Liang, Zhetong Hong et al.

With the application of big data technology and the improvement of its level, more and more enterprises and organizations begin to use big data analysis and mining technology to support business decisions. Big data technology has found applications in the electric power industry, particularly in predicting and evaluating the reliability of distribution systems. By utilizing big data information from the distribution network, the reliability prediction and evaluation method based on big data technology can possess the capability to pinpoint critical factors that exert a substantial influence on reliability indicators. These factors are then used to establish an efficient prediction model[1]. Among them, the rough set information entropy theory can help determine factors that are highly correlated and independent of each other, thereby improving the accuracy and reliability of predictions. Furthermore, real-time monitoring, fault prediction and diagnosis, fault location, and recovery of distribution systems can be effectively conducted using big data technology. These applications contribute to enhancing the efficiency and stability of power system. To sum up, the application of big data technology provides new ideas and methods for reliability analysis and management of distribution systems, and is expected to provide better solutions for reliability improvement and improvement in the power field.

Naser Seifollahi

An important and fundamental issue in the field of dairy products marketing is to have a positive consumer image of the brand. Brand image is an important factor in deciding the consumer to choose from goods and shows their general knowledge about a particular brand. The purpose of this study was to investigate the effect of internal marketing on brand image through social responsibility mediation. The type of research was applied in terms of purpose and descriptive-correlational in terms of the nature of the method. The statistical population of the study included consumers of dairy products Ardebil city. Based on Morgan's table, a sample of 384 were selected by available random sampling method to answer the questionnaire, which finally 380 of the questionnaire could be used. The results of structural equation modeling test using PLS software showed that internal marketing has a significant effect on brand image and social responsibility. Social responsibility also has a direct impact on the brand image and social responsibility plays a mediating role in the relationship between internal marketing and brand image.The findings of this study can be effective in attracting more and more dairy products manufacturing companies to the discussion of social responsibility and strengthen the desired mental image of the brand of companies.

sanaz heydari, homayoon moradnezhadi, samireh saymohammadi

The aim of this study was to identify the opportunities of agricultural production businesses in the rural cooperatives of Ilam province. In this study, the Delphi method was used. The target population of this study includes; employees working in the Agricultural Jihad Organization of Ilam province and cities, employees working in the management of rural cooperatives of the province and cities, CEO and board of directors of the union and rural cooperatives, board of agricultural experts and the trade union system of Ilam province. A total of 110 people were selected as experts using purposive sampling method. The main tools of the study are interviews and semi-structured and structured questionnaires that were designed in three rounds. To analyze the Delphi stages, the statistical indices of mean, standard deviation and coefficient of variation were used to determine the degree of consensus of the participants using SPSS software. The results showed that cases such as; Development of mushroom production and problem solving, development of native poultry production, development of honey production and problem solving of honey treatment, development of medicinal plants, development of livestock breeding (sheep, lamb and goat) and development of city gardens such as grapes and pomegranates and fruits and products market Garden, are some of the opportunities are available in all counties.

marjan adhammaleki, Bahman KHosravipour, Mansour Ghanian

Cooperatives are one of the types of participatory management systems, which provide farmers with a role in decision-making and agricultural management by providing mechanisms. In this regard, water users' organizations are formed with the aim of reducing the role of government and increasing the role of water consumers and other local institutions in the management of irrigation networks and play a key role in optimal water consumption. Therefore, this study was conducted to evaluate the factors affecting the performance of production cooperatives in the catchment area of Khuzestan with emphasis on the management of irrigation and drainage networks. The statistical population of the study consisted of all members of production cooperatives within the irrigation and drainage networks of Khuzestan (N = 5831). The sample size was determined using the sampling table of Bartlett et al. (2001) and by stratified random sampling method with proportional assignment (according to member production cooperatives), 361 people. The results of factor analysis indicate that the factors affecting the performance of production cooperatives in the catchment area of Khuzestan province include economic and financial, socio-cultural, legal-political, managerial and technical factors. The results of multi-criteria analysis using AHP technique also showed that the management criterion is the highest priority for the success of production cooperatives compared to other criteria. In fact, this finding shows that the prerequisite for the success of production cooperatives in Khuzestan is the improvement and empowerment of management structures in the areas of planning, organizing, coordinating, controlling, implementing and evaluating production cooperatives.

Christophar Nicholas Hendstein, Hiroshi Akeera Katsu

Function - The goal of this particular paper is presenting a novel framework for strategic decision making utilizing Big Data Analytics methodology. Design/methodology/approach - In this particular research, 2 distinct machine learning algorithms, Random Forest as well as Artificial Neural Networks are used to forecast export volumes working with a considerable level of open industry information. The forecasted values are in the Boston Consulting Group Matrix to conduct strategic industry analysis. Results - The proposed technique is validated utilizing a hypothetical case study of a Chinese business exporting freezers and refrigerators. The results indicate the proposed methodology makes exact trade forecasts and helps to conduct strategic industry evaluation properly. Furthermore, the RF performs much better compared to the ANN in terminology of forecast accuracy. Investigate limitations/implications - This analysis provides just one case study to evaluate the proposed methodology. In future scientific studies, the validity of the suggested technique is further generalized in various item groups and nations. Functional implications - In present day extremely competitive business environment, a good strategic industry evaluation involves exporters or importers making much better predictions along with strategic choices. To us the proposed BDA based strategy, businesses may efficiently determine business opportunities and alter their strategic choices appropriately. Originality/value - This's the very first study to provide a holistic methodology for strategic industry evaluation using BDA. The proposed methodology effectively forecasts global trade volumes and helps with the strategic decision making practice through succeeding insights into worldwide marketplaces.

mojtaba shamsnejad, reza Esfanjari Kenari, Mohammad Kavoosi Kalashami

Considering the important role of rural cooperatives in sustainable rural development, Evaluating the efficiency and performance of these organizations plays an important role in the decision making of planners and managers.The main purpose of this study is to investigating the profit efficiency of rural cooperative and its Effective Factors in Guilan Province using stochastic frontier function production method and the behavioral profit model. The statistical population of the present study was 110 active rural cooperatives in Guilan province in 2017 and the sampling method was census. The results showed the average of profit efficiency of rural cooperatives in Guilan province is 63%. Also, the results of estimating the profit function showed the current capital and costs related to the activities of these cooperatives have a direct and significant relationship with frontier profit. In addition, investigating the effective factors on profit inefficiency of rural cooperatives showed the duration of activity of these cooperatives, second job, manager's wage and management Experience have a positive and significant effect on profit inefficiency. On the other hand, the number of shareholders, number of villages, cooperative distance to city, establishment of cooperative in the center of province, education of manager and the number of training courses for cooperative managers have a negative and significant effect on profit inefficiency of rural cooperatives in Guilan province